🤖 AI Summary
This work addresses the lack of efficient and unified tools for estimating restricted mean survival time (RMST) and restricted mean time lost (RMTL) under both standard survival and competing risks settings, particularly the absence of methods for cause-specific RMTL and its causal effects. Building upon the mets R package, we present the first implementation capable of simultaneously computing RMST/RMTL and their standard errors over the full time range. Our approach integrates inverse probability censoring weighting (IPCW), G-computation, and influence function theory to enable nonparametric estimation, regression modeling, standardization, and inference for average treatment effects (ATE). The proposed method achieves linear time complexity, substantially enhancing computational efficiency and statistical flexibility for large-scale data analysis.
📝 Abstract
This paper introduces software implemented in the mets R-package for calculating non-parametric and regression estimates of Restricted Mean Survival Time (RMST) and Restricted Mean Time Lost (RMTL), including RMTL due to specific causes. A unique feature is the ability to compute the non-parametric estimates of RMST and RMTL, as well as their standard errors, for all time horizons simultaneously. Regression modeling in mets is based on Inverse Probability of Censoring Weighting (IPCW) methods. The package implements different versions of IPCW adjusted estimating equations. A critical technical contribution is the provision of influence functions for all models, which enables the computation of standard errors and allows the estimates to be used as building blocks for more complex statistics, such as the while-alive estimate in recurrent events settings. To expand capabilities in causal inference, the mets package also implements methods for standardization estimates (G-computation) and the estimation of Average Treatment Effects (ATE) for both RMST and RMTL in the competing risks setting. Importantly, the computations scale linearly with the number of observations, making the software efficient for use with large datasets.